Astra DB Vectorize collections need no separate embedder attached in your pipeline

Astra DB Vectorize collections need no separate embedder attached in your pipeline

TL;DR

When an Astra DB collection is set up with a Vectorize embedding integration, your application must not also attach its own embedding model: the collection generates vectors itself, and double-embedding wastes calls and can corrupt similarity results. The Langflow Astra DB component docs say: "Attach an embedding model component to generate embeddings. If the collection has a vectorize integration, don't attach an embedding model component." Related detail from the same docs: the similarity metric (cosine, dot_product, euclidean) is a collection-level setting, so it must be chosen when the collection is created, not per query.

Use this when you run into the situation in the title.

When not to use this skill: unrelated tasks. It covers only the procedure above.

Compatibility

The steps above apply to the commands named in them. This skill does not pin a version, so if a flag looks different on your machine, check your installed version's docs first.

When an Astra DB collection is set up with a Vectorize embedding integration, your application must not also attach its own embedding model: the collection generates vectors itself, and double-embedding wastes calls and can corrupt similarity results. The Langflow Astra DB component docs say: "Attach an embedding model component to generate embeddings. If the collection has a vectorize integration, don't attach an embedding model component." Related detail from the same docs: the similarity metric (cosine, dotproduct, euclidean) is a collection-level setting, so it must be chosen when the collection is created, not per query. Verified answer: When an Astra DB collection is set up with a Vectorize embedding integration, your application must not also attach its own embedding model: the collection generates vectors itself, and double-embedding wastes calls and can corrupt similarity results. The Langflow Astra DB component docs say: "Attach an embedding model component to generate embeddings. If the collection has a vectorize integration, don't attach an embedding model component." Related detail from the same docs: the similarity metric (cosine, dotproduct, euclidean) is a collection-level setting, so it must be chosen when the collection is created, not per query. Source: https://github.com/langflow-ai/langflow/blob/HEAD/docs/docs/Components/bundles-datastax.mdx